arXiv subjects

Christof Imhof

Publications and source records attributed to Christof Imhof.

3 recordsLinked to original sources

arXiv2025

SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services

The increasing demand for connected vehicular services poses significant challenges for AI-based network and service management due to the high volume and rapid variability of network state information. Traditional management and control mechanisms struggle to scale when processing fine-grained metrics such as Channel Quality Indicators (CQIs) in dynamic vehicular environments. To address this challenge, we propose SCAR (State-Space Compression for AI-Based Network Management), an edge-assisted framework that improves scalability and fairness in vehicular services through network state abstraction. SCAR employs machine-learning (ML)-based compression techniques, including clustering and radial basis function (RBF) networks, to reduce the dimensionality of CQI-derived state information while preserving essential features relevant to management decisions. The resulting compressed states are used to train reinforcement learning (RL)-based management policies that aim to maximize network efficiency while satisfying service-level fairness objectives defined by the NGMN. Simulation results show that SCAR increases the time spent in feasible management regions by 14% and reduces unfair service allocation time by 15% compared to reinforcement learning baselines operating on uncompressed state information. Furthermore, simulated annealing with stochastic tunneling (SAST)-based clustering reduces state compression distortion by 10%, confirming the effectiveness of the proposed approach. These results demonstrate that SCAR enables scalable and fair AI-assisted network and service management in dynamic vehicular systems.

cs.LG↗

arXiv2025

Detection of Disengagement from Voluntary Quizzes: An Explainable Machine Learning Approach in Higher Distance Education

Students disengaging from their tasks can have serious long-term consequences, including academic drop-out. This is particularly relevant for students in distance education. One way to measure the level of disengagement in distance education is to observe participation in non-mandatory exercises in different online courses. In this paper, we detect student disengagement in the non-mandatory quizzes of 42 courses in four semesters from a distance-based university. We carefully identified the most informative student log data that could be extracted and processed from Moodle. Then, eight machine learning algorithms were trained and compared to obtain the highest possible prediction accuracy. Using the SHAP method, we developed an explainable machine learning framework that allows practitioners to better understand the decisions of the trained algorithm. The experimental results show a balanced accuracy of 91\%, where about 85\% of disengaged students were correctly detected. On top of the highly predictive performance and explainable framework, we provide a discussion on how to design a timely intervention to minimise disengagement from voluntary tasks in online learning.

cs.AI↗

arXiv2022

Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.

stat.ML↗